where \(w_r\)  and \(w_z\)  are the learnt weights of the neural network, b the bias term and \(z\)  the sensitive attribute and \(f(\cdot )\)  the transfer function (e.g., linear or sigmoid). The weights \(w_r\)  and \(w_z\)  of \(r\)  and \(z\) , respectively, indicate how large the influence of the sensitive attribute on the classification is [1]}.
In the following experiments a model trained without the affinity loss using the same architecture as the fair one is reffed as baseline.
